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该研究提出卷积QFT编译策略，在IBM量子平台上实现最多100比特QFT实验，50比特保真度11.4%、80比特1.8%，为迄今最大规模实验QFT演示。
AI 中文摘要
我们提出并实验验证了“卷积量子傅里叶变换（Convolutional QFT）”：一种针对线性近邻（LNN）量子比特拓扑结构的量子傅里叶变换（QFT）子程序的构造性编译策略。我们首先引入一种新策略，该策略仅使用n²−n个受控非门（CX门）即可将n比特QFT编译到LNN拓扑结构上，与全连接架构直接编译的要求相匹配。随后，我们推导了实验中使用的卷积变体，该变体总共需要额外2个CX门，通过一种紧凑、平移不变的核电路元件实现，该元件遍历量子寄存器。我们在IBM量子平台上通过执行QFT基准测试电路，展示了卷积编译策略的性能：测得50比特时的过程保真度为11.4%，80比特时为1.8%；直至100比特时，正确输出态仍能在背景噪声之上清晰区分。这些结果构成了迄今为止在任何量子计算硬件上演示的最大规模实验QFT。
英文摘要
We present and experimentally validate the `Convolutional QFT': a constructive compilation strategy for the Quantum Fourier Transform (QFT) subroutine on a linear nearest neighbor (LNN) qubit topology. We first introduce a novel strategy that compiles the $n$-qubit QFT onto an LNN topology using only $n^2 - n$ $CX$ gates, matching requirements of a direct compilation on an all-to-all architecture. We then derive the convolutional variant used in our experiments, which requires an additional two $CX$ gates in total, and is realized via a compact, translation-invariant kernel circuit gadget that traverses a quantum register. We demonstrate the power of the convolutional compilation strategy on the IBM Quantum Platform by executing QFT benchmarking circuits. We measure a process fidelity of 11.4% at 50 qubits, and 1.8% at 80 qubits. The correct output state remains clearly distinguishable above background noise up to 100 qubits. These results constitute the largest experimental QFT demonstrated on any quantum computing hardware to date.
Commentsv2: Correct typos, update figures, add author contribution statement, edits to Conclusion section
